auto_annot_Martin2019_with_Smillie2019_dblabel_l2

import besca as bc
import scanpy as sc
import pkg_resources
import os

Specify folders where .h5ad files are found and their names.

The datasets that are already annotated and should be used for training. If you only use one dataset please use list of one.

adata_trains = [sc.read(pkg_resources.resource_filename('besca', 'datasets/data/Smillie2019_processed.h5ad'))]

The dataset of interest that should be annotated.

adata_pred = bc.datasets.Martin2019_processed()
adata_orig = bc.datasets.Martin2019_processed()

Define level of dblabel reference annotation

level = 2

Give your analysis a name.

analysis_name = 'auto_annot_Martin2019_with_Smillie2019_dblabel_l' + str(level)

Now specify parameters

Specify column name of celltype annotation you want to train on.

celltype_train ='dblabel_l' + str(level)
celltype_test = 'dblabel_l' + str(level)

Choose a method:

• linear: Support Vector Machine with Linear Kernel
• sgd: Support Vector Machine with Linear Kernel using Stochastic Gradient Descent
• rbf: Support Vector Machine with radial basis function kernel. Very time intensive, use only on small datasets.
• logistic_regression: Standard logistic classifier iwth multinomial loss.
• logistic_regression_ovr: Logistic Regression with one versus rest classification.
• logistic_regression_elastic: Logistic Regression with elastic loss, cross validates among multiple l1 ratios.

method = 'logistic_regression'

Specify merge method if using multiple training datasets. Needs to be either scanorama or naive.

merge = 'scanorama'

Decide if you want to use the raw format or highly variable genes. Raw increases computational time and does not necessarily improve predictions.

use_raw = False

You can choose to only consider a subset of genes from a signature set.

genes_to_use = 'all'

Translate cell type annoation to lower dblabel level

new_cnames = bc.tl.sig.obtain_new_label(
    nomenclature_file=pkg_resources.resource_filename('besca', 'datasets/nomenclature/CellTypes_v1.tsv'), 
    cnames=list(adata_trains[0].obs['dblabel'].cat.categories), 
    reference_label='dblabel', 
    new_label='dblabel', 
    new_level=level)

new_cnames

	new_label
CD1c-positive myeloid dendritic cell	myeloid leukocyte
CD4-positive, alpha-beta memory T cell	lymphocyte
CD8-positive, alpha-beta T cell	lymphocyte
CD8-positive, alpha-beta cytokine secreting effector T cell	lymphocyte
CD141-positive myeloid dendritic cell	myeloid leukocyte
HEV endothelial cell	endothelial cell of vascular tree
activated CD4-positive, alpha-beta T cell	lymphocyte
brush cell	endo-epithelial cell
endothelial cell	endothelial cell
enterocyte	endo-epithelial cell
enterocyte progenitor	endo-epithelial cell
enteroendocrine cell	glandular epithelial cell
exhausted-like CD4-positive, alpha-beta T cell	lymphocyte
fibroblast	fibroblast
follicular B cell	lymphocyte of B lineage
germinal center B cell	lymphocyte of B lineage
glial cell	glial cell
goblet cell	glandular epithelial cell
immature enterocyte	endo-epithelial cell
immature goblet cell	glandular epithelial cell
inflammatory fibroblast	fibroblast
inflammatory monocyte	myeloid leukocyte
innate lymphoid cell	lymphocyte
macrophage	myeloid leukocyte
mast cell	myeloid leukocyte
microfold cell	endo-epithelial cell
microvascular endothelial cell	endothelial cell of vascular tree
myofibroblast cell	myofibroblast cell
natural killer cell	lymphocyte
pericyte cell	pericyte cell
plasma cell	lymphocyte of B lineage
proliferating B cell	lymphocyte of B lineage
proliferating T cell	lymphocyte
proliferating monocyte	myeloid leukocyte
proliferating transit amplifying cell	endo-epithelial cell
regulatory T cell	lymphocyte
stem cell	stem cell
transit amplifying cell	endo-epithelial cell

adata_trains[0].obs['dblabel_l' + str(level)] = bc.tl.sig.add_anno(adata_trains[0], new_cnames, 'new_label', 'dblabel')

new_cnames = bc.tl.sig.obtain_new_label(
    nomenclature_file=pkg_resources.resource_filename('besca', 'datasets/nomenclature/CellTypes_v1.tsv'), 
    cnames=list(adata_pred.obs['dblabel'].cat.categories), 
    reference_label='dblabel', 
    new_label='dblabel', 
    new_level=level)

new_cnames

	new_label
CD4-positive, alpha-beta memory T cell	lymphocyte
CD8-positive, alpha-beta cytotoxic T cell	lymphocyte
CD8-positive, alpha-beta memory T cell	lymphocyte
HEV endothelial cell	endothelial cell of vascular tree
IgG plasma cell	lymphocyte of B lineage
IgM or IgA plasma cell	lymphocyte of B lineage
blood vessel endothelial cell	endothelial cell of vascular tree
fibroblast	fibroblast
group 1 innate lymphoid cell	lymphocyte
group 3 innate lymphoid cell	lymphocyte
intestinal epithelial cell	endo-epithelial cell
memory B cell	lymphocyte of B lineage
memory T cell	lymphocyte
myeloid dendritic cell	myeloid leukocyte
myeloid leukocyte	myeloid leukocyte
naive B cell	lymphocyte of B lineage
naive T cell	lymphocyte
neural cell	neural cell
pericyte cell	pericyte cell
plasmacytoid dendritic cell	lymphocyte
proliferating T cell	lymphocyte
regulatory T cell	lymphocyte

adata_pred.obs['dblabel_l' + str(level)] = bc.tl.sig.add_anno(adata_pred, new_cnames, 'new_label', 'dblabel')
adata_orig.obs['dblabel_l' + str(level)] = adata_pred.obs['dblabel_l' + str(level)] 

Merge all training and the testing set.

This function merges training datasets, removes unwanted genes, and if scanorama is used corrects for datasets.

adata_train, adata_pred = bc.tl.auto_annot.merge_data(adata_trains, adata_pred, genes_to_use = genes_to_use, merge = merge)

merging with scanorama
[[0.         0.55297257]
 [0.         0.        ]]
Processing datasets (0, 1)
integrating training set
calculating intersection

Train the classifier.

The returned scaler is fitted on the training dataset (to zero mean and scaled to unit variance).

classifier, scaler = bc.tl.auto_annot.fit(adata_train, method, celltype_train)

[Parallel(n_jobs=10)]: Using backend LokyBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   5 out of   5 | elapsed: 41.1min finished

Prediction

Use fitted model to predict celltypes in adata_pred. Prediction will be added in a new column called 'auto_annot'. Paths are needed as adata_pred will revert to its original state (all genes, no additional corrections). The threshold should be set to 0 or left out for SVM. For logisitic regression the threshold can be set.

adata_predicted = bc.tl.auto_annot.adata_predict(classifier = classifier, scaler = scaler, adata_pred = adata_pred, adata_orig = adata_orig, threshold = 0)

Write out metrics to a report file, create confusion matrices and comparative umap plots

%matplotlib inline

bc.tl.report(
    adata_pred=adata_predicted,
    celltype=celltype_test, 
    method=method, 
    analysis_name=analysis_name,
    train_datasets = adata_trains,
    test_dataset = adata_orig,
    merge = merge,
    name_prediction='auto_annot', 
    name_report='auto_annot', 
    use_raw=use_raw,
    remove_nonshared=True, 
    clustering='leiden', 
    asymmetric_matrix=True, 
    delimiter='\t', 
    verbose=True
)

acc: 0.89
f1: 0.9
ami: 0.75
ari: 0.77
silhouette dblabel_l2: 0.32
silhouette auto_annot: 0.11
pair confusion matrix:
            0           1
0  2327989860   119844072
1   287748096  1133444574

... storing 'auto_annot' as categorical

WARNING: saving figure to file figures/umap.ondata_auto_annot_Martin2019_with_Smillie2019_dblabel_l2.png

WARNING: saving figure to file figures/umap.auto_annot_Martin2019_with_Smillie2019_dblabel_l2_dblabel_l2.png

WARNING: saving figure to file figures/umap.auto_annot_Martin2019_with_Smillie2019_dblabel_l2_auto_annot.png

WARNING: saving figure to file figures/umap.auto_annot_Martin2019_with_Smillie2019_dblabel_l2_leiden.png

sc.pl.umap(adata_predicted, color=[celltype_test, 'auto_annot'])
sc.pl.umap(adata_predicted, color=[celltype_test, 'auto_annot'], legend_loc='on data', legend_fontsize=8)

sc.pl.umap(adata_predicted, color=[celltype_test, 'auto_annot'], legend_fontsize=7, wspace = 1.4, save = '.svg')
sc.pl.umap(adata_predicted, color=[celltype_test, 'auto_annot'], legend_loc='on data', legend_fontsize=7, wspace = 1.4, save = '.ondata.svg')

WARNING: saving figure to file figures/umap.svg

WARNING: saving figure to file figures/umap.ondata.svg

adata_train

View of AnnData object with n_obs × n_vars = 149732 × 1054
    obs: 'CELL', 'Cluster', 'Health', 'Location', 'Subject', 'celltype_highlevel', 'nGene', 'nUMI', 'original_name', 'percent_mito', 'n_counts', 'n_genes', 'batch', 'leiden', 'dblabel', 'celltype', 'cluster_celltype', 'Type', 'dblabel_l2'
    var: 'SYMBOL', 'ENSEMBL-0', 'n_cells-0', 'total_counts-0', 'frac_reads-0', 'ENSEMBL-1', 'n_cells-1', 'total_counts-1', 'frac_reads-1'
    uns: 'Cluster_colors', 'Location_colors', 'Type_colors', 'celltype_highlevel_colors', 'leiden', 'leiden_colors', 'umap'
    obsm: 'X_pca', 'X_umap', 'X_scanorama'

# Compare to random assignment
import random
random.seed(1)
adata_predicted.obs['random_labeling'] = list(adata_predicted.obs[celltype_test].sample(frac=1))

bc.tl.report(
    adata_pred=adata_predicted,
    celltype=celltype_test, 
    method="compare_to_random_" + method, 
    analysis_name=analysis_name,
    train_datasets = adata_trains,
    test_dataset = adata_orig,
    merge = merge,
    name_prediction="random_labeling",
    name_report="compare_to_random_auto_annot", 
    use_raw=use_raw,
    remove_nonshared=True, 
    clustering='leiden', 
    asymmetric_matrix=True, 
    delimiter='\t', 
    verbose=True)

acc: 0.36
f1: 0.36
ami: -0.0
ari: -0.0
silhouette dblabel_l2: 0.32
silhouette random_labeling: -0.04
pair confusion matrix:
            0          1
0  1548330116  899503816
1   899503816  521688854

... storing 'random_labeling' as categorical

WARNING: saving figure to file figures/umap.ondata_auto_annot_Martin2019_with_Smillie2019_dblabel_l2.png

WARNING: saving figure to file figures/umap.auto_annot_Martin2019_with_Smillie2019_dblabel_l2_dblabel_l2.png

WARNING: saving figure to file figures/umap.auto_annot_Martin2019_with_Smillie2019_dblabel_l2_random_labeling.png

WARNING: saving figure to file figures/umap.auto_annot_Martin2019_with_Smillie2019_dblabel_l2_leiden.png

from sinfo import sinfo
sinfo()

-----
anndata             0.7.5
besca               2.4+57.g5ad53b2
pandas              1.2.2
pkg_resources       NA
plotly              4.14.3
scanpy              1.6.1
sinfo               0.3.1
sklearn             0.24.1
-----
IPython             7.20.0
jupyter_client      6.1.11
jupyter_core        4.7.1
notebook            6.2.0
-----
Python 3.7.9 | packaged by conda-forge | (default, Dec  9 2020, 21:08:20) [GCC 9.3.0]
Linux-3.10.0-693.11.6.el7.x86_64-x86_64-with-centos-7.4.1708-Core
24 logical CPU cores, x86_64
-----
Session information updated at 2021-07-18 08:04

Convert to html

%%javascript

IPython.notebook.kernel.execute('nb_name = "' + IPython.notebook.notebook_name + '"')

nb_name = os.path.join(os.getcwd(), nb_name)

! jupyter nbconvert --to html {nb_name}

[NbConvertApp] Converting notebook /pstore/data/bioinfo/users/hatjek/devel/besca_publication_results/intestine/auto_annot/auto_annot_Martin2019_with_Smillie2019_dblabel_l2.ipynb to html
[NbConvertApp] Writing 10089677 bytes to /pstore/data/bioinfo/users/hatjek/devel/besca_publication_results/intestine/auto_annot/auto_annot_Martin2019_with_Smillie2019_dblabel_l2.html